Abstract

Aggression and other acute harms experienced in the
night-time economy are topics of significant public health concern.
Although policies to minimise these harms are frequently proposed,
there is often little evidence available to support their
effectiveness. In particular, indirect and displacement effects are
rarely measured. This paper describes a proof-of-concept agent-based
model ‘SimDrink’, built in NetLogo, which simulates a population of
18-25 year old heavy alcohol drinkers on a night out in Melbourne to
provide a means for conducting policy experiments to inform policy
decisions.
The model includes demographic, setting and situational-behavioural
heterogeneity and is able to capture any unintended consequences of
policy changes. It consists of individuals and their friendship groups
moving between private, public-commercial (e.g. nightclub) and
public-niche (e.g. bar, pub) venues while tracking their alcohol
consumption, spending and whether or not they experience
consumption-related harms (i.e. drink too much), are involved in verbal
violence, or have difficulty getting home. When compared to available
literature, the model can reproduce current estimates for the
prevalence of verbal violence experienced by this population on a
single night out, and produce realistic values for the prevalence of
consumption-related and transport-related harms. Outputs are robust to
variations in underlying parameters. Further work with policy makers is
required to identify several specific proposed harm reduction
interventions that can be virtually implemented and compared. This will
allow evidence based decisions to be made and will help to ensure any
interventions have their intended effects.

Introduction

Aggression and other acute harms experienced by young
adults in
the night-time economy are topics of significant public health concern (Australian Institute of Health and
Welfare 2013).
Although policies to minimise these harms are frequently proposed,
there is often little evidence available to support their effectiveness
(Miller et al. 2015). This
is partly due to
the characteristics of Australia's drinking culture (Room
1988), which reduces the applicability of evidence from many
international studies or policy evaluations. Australian evidence for
the impact of policies in this area is largely based on natural
experiments, where researchers have evaluated the impact of policies
after they have been implemented (Kypri
et al. 2011; Livingston
2008). This is critical
work, but is only useful for post-hoc policy evaluations. In contrast,
simulation models provide a means for assessing the likely impact of
otherwise untested policies (Dray et
al. 2012).

An overarching difficulty in testing and comparing
night-time
economy related policies is that the same policy can affect different
settings in different ways. For example, although increases in
on-licence alcohol prices can lead to people consuming less in these
settings, this is offset to some extent by substitution of drinking in
public venues for drinking in private venues (Meier
et al. 2010) or people drinking at private venues before
going out
to save money (MacLean &
Callinan 2013; Miller et
al. 2013). These indirect effects are
associated with a different set of harms and need to be weighed against
any benefits. Policies can also address specific types of harm that are
more prevalent in particular settings. For example, being stranded in
the central business district (CBD) after public transport has finished
is less likely for those attending private drinking settings. One
consequence of setting heterogeneity and interaction is that any model
testing policy changes or combinations of changes needs to consider
indirect and displacement effects, and should ideally include multiple
settings.

Changes in the night-time economy have different effects
upon
people of different income, socioeconomic background, geographic place
of residence, gender and so on (Hart
2015; Meier et al. 2010).
Many policy changes may have
a greater effect on a subset of the population; for example changes to
alcohol pricing will have more affect upon those with less money, and
changes to transport options will have more effect on those who live
further away from where they drink (Callinan
et
al. 2015; MacLean et al.
2013; MacLean &
Moore 2014). Models that do not
affect individuals differently are prone to error if the results are
extrapolated, since they do not properly account for the dilution of
effects across the entire population.

Typical models used to test alcohol policy options often
inadequately capture these differences in population and setting
characteristics. In particular, most modelling involves little
consideration of important variables such as drinking setting and
context that are known to impact consumption (Callinan
et al. 2014). One way to address this issue is to use
agent-based
models (ABMs). ABMs use a set of autonomous 'agents' to represent a
population and offer a powerful and more complex method for describing
human behaviour and local interaction (Gilbert
2008). Agents follow simple behavioural rules and make
decisions
about how to interact with each other and their environment. Using
ABMs, policies can be implemented that only effect the decisions of
agents at particular times and in particular settings. Large scale
patterns can then emerge from a multitude of local, stochastic
interactions. Further, multiple settings and agents with different
characteristics can be implemented together, providing a more realistic
implementation in a larger environment.

Using ABMs to address public health policy questions is not
new;
for example, these types of models have provided great insights into
infectious disease transmission (Castiglione
et al. 2007; Kretzschmar
& Wiessing
1998; Rolls et al. 2013)
and illicit drug
use (Dray et al. 2008; Dray
et al. 2012; Galea et al.
2009; Moore et al. 2009).
In the context of alcohol
use, ABMs have been useful in understanding the influence of social
networks on levels of consumption, for example in estimating both how
social networks can be used to predict heavy drinking behaviours (Mercken et al. 2015; Ormerod
& Wiltshire 2009), and how heavy drinkers promote
increased
drinking through their social networks (Giabbanelli
& Crutzen 2013; Gorman
et al. 2006).
On a population level, the Organisation for Economic Co-operation and
Development (OECD) recently used similar simulation modelling
techniques to estimate the economic and public health benefits of
reduced alcohol consumption (Cecchini
&
Sassi 2015; OECD 2015),
finding that even small
decreases in consumption are likely to provide significant benefits.
However, the existing literature is focussed on longer term (meaning
more than a day) behavioural changes within individuals. There has been
a shift in contemporary alcohol and other drug research towards
considering the consumption event as the unit of analysis (Bøhling 2014; Callinan
et al. 2014; Dilkes-Frayne
2014; Kuntsche et al.
2014); researchers are
attempting to understanding individuals' decisions and their
consequences within a single drinking event (a 'big night out'), and
how interventions throughout the night might affect outcomes. Models
with a temporal resolution designed to capture changes to social
networks are less appropriate for this, since on the scale of a single
drinking event it is reasonable to approximate social groups as being
well established and the psychosocial characteristics of drinking as
highly entrenched within each group. Instead, there is a need to model
how different enabling or restricting alcohol policies—that act on the
environment, rather than to the individual—may influence the night out
of an already established group of heavy drinkers.

This paper describes an ABM model 'SimDrink', built using
NetLogo
(version 5.1.0) (Tisue &
Wilensky 2004)
and run with the RNetLogo package (Thiele
2014),
that simulates a population of 18-25 year olds engaging in heavy
sessional drinking on a night out in Melbourne. The model consists of
individuals and their friendship groups moving between private,
public-commercial (e.g. nightclub) and public-niche (e.g. bar, pub)
venues while tracking their alcohol consumption, spending and whether
or not they experience consumption-related harms (i.e. drink too much),
are involved in verbal violence, or have difficulty getting home.
Importantly, individuals' behaviour and decisions will be setting
dependent and allowed to vary as the night progresses, influenced by
their own—and also their friends'—alcohol consumption, finances and
harms experienced. With this model we will be able to test and quantify
the direct and indirect effects of policies such as 24 hour public
transport, public venue lockouts, changes to responsible service of
alcohol enforcement, public venue closing times and drink prices.
Further, although the model environment is based on Melbourne's
characteristics, it is highly generalizable and with minor
modifications and locally valid parameters could easily be used to test
policies in other locations.

Model
description

Model environment

The model environment consists of a circular Inner City
(IC) area
of radius 5km and an Outer Urban (OU) area extending radially between
5km and 25km from the centre. The IC area contains a mixture of venue
types where people can consume alcohol: public venues that are
classified as either niche (e.g. bars, pubs) or commercial (e.g.
nightclubs); and private venues (e.g. house parties). The OU area
contains only private venues since OU public venues in Melbourne are
less popular among the young population being modelled, who would
typically commute to the IC to attend public venues instead (MacLean & Moore 2014).
All venues are
distributed randomly throughout their respective regions (IC or OU).
There is a taxi rank in the centre of the model that acts as a gateway
for people leaving public venues after public transport stops running.
Although travel time is calculated for all movements, transport issues
occurring at other times or locations are not considered in this model
(i.e. public transport is assumed to be adequate when it is operating,
and all travel departing from private venues is assumed to be
non-problematic). Finally, there is a node near the centre of the city
where individuals who leave public venues unable to afford transport
home wait for the first train.

Agent properties

At the start of the night each agent is allocated some
fixed
properties and some counters to track their night. Their fixed
properties are gender, age (18–21 years or 22–25 years), residence (IC
or OU), drinking rate, personal drinking limit, initial spending money,
size of initial friendship group and planned length of night, and their
counters track remaining spending money, total drinks consumed, total
hours spent drinking and whether harms have been experienced (verbal,
drinking too much or difficulty getting home). The distributions used
to allocate fixed properties are listed in Appendix A.

Each agent forms fixed links to all of their friends
(friendship
groups remain linked throughout the night) and each friendship group is
allocated a starting time. There is also a single temporarily link
connecting agents to their current venue. Friendship groups enter the
model together at their start time and once an individuals' night is
over they are able to leave the model, disconnecting links to their
friends and final venue.

Venue properties

Venues are also allocated fixed properties and counters.
Their
fixed properties are location (IC or OU), setting (private,
public-niche or public-commercial), closing time (11pm, 12am, 1am, 3am
or 5am for public venues or infinite for private venues), drink limit
(the maximum number of drinks people in the venue can have before being
thrown out—different values for 18–21 year olds and 22–25 year olds in
public venues; infinite for private venues) and drink price, and their
counters are number of drinks sold, number of verbal fights in the
venue and number of patrons ejected for having total alcohol
consumption over their drink limit. The distributions used to allocate
fixed properties are listed in Appendix A.

Time frame of model

Each time step in the model represents an hour. A complete
simulation commences at \(t=0\) corresponding to 5pm and the model runs
until all agents have finished their night out. This occurs when they
either go home or become stuck in the city waiting for public transport
to start the morning.

Model assumptions and the psychosocial characteristics of
drinking in Australia

Individuals drink at different rates in different
settings
(i.e. in public-niche versus public-commercial) and when intoxicated (Lindsay 2005);

Friendship groups don't split up when changing venues,
with
the exception of some members going home (Miller
et al. 2013—the most common reasons for young people to
attend
drinking environments is either to socialise with friends or for
special events/celebrations);

Due to both peer-pressure and safety concerns (in
particular
among OU residents), after exceeding their planned length of night
people will only go home if at least one friend has also exceeded their
planned length of night (Duff
& Moore 2015—also
based on extensive fieldwork from AH and JW); and

Given the high cost of taxis in Melbourne, most people
will
be aware of the last train departure time and many people are likely to
make specific efforts to catch the last train home (Duff
& Moore 2015—also based on extensive fieldwork from
AH and JW).

The extent to which these features are unique to Australia
may
limit the generalisability of this model to other international
settings. For the model to be applied elsewhere, the relevance of these
features (along with parameter estimates) would need to be considered.

Setting up a simulation

Each simulation is set up by: 1) generating and
distributing
venues throughout the model and allocating them their fixed properties;
2) generating a seed population of OU and IC residents and assigning
them each a friendship group size; 3) assigning the seed population to
start locations for their night; 4) creating additional agents
('friends') in the same location who are linked to the seed agents; 5)
allocating fixed properties (age, sex, drinking behaviours and spending
money) to all agents; and 6) making agents who do not commence their
drinking at \(t=0\) inactive at their current location (where they will
not interact with anything until their starting time). Each of these
steps is done according to the parameters in Appendix A.

Agent behaviour

Once the model is started seven main operations are
performed
each time step. Each of these steps is schematically represented in the
flow diagrams in Appendix B, and the corresponding parameters for each
decision are provided in Appendix A.

Offer public venues a chance to eject
intoxicated patrons
or close
Public venues identify patrons who have consumed more than the venue's
drink limit and force them to go home. If these agents have at least
one friend who has consumed more than a harms threshold, they may
experience harms as they leave (see step 4). If a public venue has
reached closing time, all current patrons are offered a choice of
whether to go home or move on to another venue—those choosing to move
to another venue do so with their remaining friends.

Offer agents a chance to move between venues
Agents who have been at a venue for an hour or more choose to either
stay at the venue or move to another (Dietze
et
al. 2014; Miller et al.
2013). Those
choosing to move take their entire friendship group with them (Miller et al. 2013), and their
new location
depends on their current setting type, their residence and the types of
venues still open. The model assumes: agents only visit private
locations near their residence (i.e. IC agents only go to private
venues in the IC); agents don't move from OU private venues to the city
once public transport has stopped; there is no gender differences in
places visited; IC to IC travel is not done by taxi unless an IC
resident is going home (when they choose whether to get a taxi or not);
travel time between venues depends on mode of transport and is a
maximum of one hour; and the cost of travel by public transport is
negligible.

Offer agents a chance to consume drinks
Agents calculate their actual drinking rates: that is, they scale their
fixed drinking rates depending on their current setting (private,
public-niche, public-commercial) and whether they are intoxicated
(agents decrease their drinking rate when they have consumed more than
half their drinking limits). Agents then attempt to buy an hours' worth
of drinks; however those who have just arrived at a venue must deduct
travel and queueing time, and those who do not have enough money will
buy only as many as they can afford.

Determine harms experienced by agents
Agents who have consumed more than their personal drinking limit are
considered to have drunk too much and will go home. Agents can also
experience verbal violence—this depends on their current location type
and whether they have consumed more than a harms drink threshold
(agents who have consumed more than 12 (men) or 6 (women) drinks are at
increased risk of verbal violence—Appendix A). Agents are considered to
have had difficulty getting home if they have spent two or more hours
waiting for a taxi.

Get agents to consider going home
Agents are forced to go home if either: they have consumed more than
their personal drink threshold; they are out of money; they and one or
more of their friends have exceeded their planned length of night (Duff & Moore 2015); or if
more than half of
their initial friendship group has gone home. Agents may decide to go
home if: they are in a public venue and the last train is about to
leave (Duff & Moore 2015,
this choice
depends on their remaining money, the planned length of their night and
where they live); they are in a public venue, public transport has
stopped and they have only enough money for a taxi left; or if they or
a friend have experienced some verbal violence.

Distribute some agents from the taxi rank to
their new
locations
Each time step agents waiting at the taxi rank have some chance of
going to their new venue (either home or a private venue). This depends
on the number of taxis (per 100 people) in the model and the current
size of the queue. Agents who have been waiting for 2 or more hours for
a taxi and have consumed more than a harms drinking threshold will loop
through step 4 again.

Activate friendship groups
Friendship groups who have a start time corresponding to the current
model time are activated and begin to interact with the rest of the
model, 'starting' their night out.

Calibration

A complete list of parameter values and their sources is
provided
in Appendix A. Public transport and venue setting properties have been
determined using publicly available information for Melbourne (Public Transport Victoria 2015;
Victorian Commission for
Gambling and Liquor
Regulation 2015), and where possible agent behaviour has been
parametrized using the Young Adults Alcohol Study (YAAS, Dietze et al. 2014). Any
remaining parameter
estimates have been taken from available literature; where no studies
were available to explicitly inform parameters, plausible estimates
were made by the authors based on their extensive experience conducting
social research on alcohol and other drug use in the night-time
economy, including ethnographic research on young people's drinking
events in OU and IC areas of Melbourne. These parameters were tested in
a sensitivity analysis and as part of a Latin Hypercube uncertainty
analysis.

YAAS is a study of 802 young (18–25 year olds) people from
Melbourne that asks specific questions about the most recent occasion
when they consumed more than 7 (women) or 10 (men) standard drinks (in
Australia, 10g of alcohol). This includes the number and types of
venues attended; number of drinks consumed; total time and money spent
in each venue; and whether or not verbal violence was experienced
during the course of the night. Due to oversampling from particular
areas, participants could be classified as residing in either the Local
Government Areas (Department of
Transport 2015)
of Yarra (\(n=127\), proxy for IC), Hume (\(n=275\), proxy for OU) or
the Rest of Melbourne (\(n=400\)). YAAS participants from Yarra or Hume
have been used to determine model parameters, while participants from
the Rest of Melbourne have had their nights compared to the outputs of
the model to determine its accuracy. This procedure avoids using the
dataset for both parameter determination and model calibration.

Due to low reports of verbal violence among Yarra and Hume
participants in the YAAS (\(N=28\) reported verbal violence on their
most recent big night out), all YAAS data were used to determine the
verbal violence harm parameters. Hence it is no longer valid to compare
model outputs for these harms to those reported by YAAS participants
from the Rest of Melbourne. However, a follow-up wave has since been
conducted (\(N=531\) (66%) of the original sample were retained), and
model outputs for verbal harms have been compared against those
reported in the follow-up data.

Among YAAS participants, verbal violence was more likely to
be
reported by older males, and on nights when more venues were visited,
more drinks were consumed, more hours were spent out and more money was
spent (Table 1). However, the
low number of
reports of verbal violence means that these differences were not
statistically significant and adjusted odds ratios provided no further
insight.

Table 1: Gender
and age categories of
individuals from the Young Adults Alcohol Study (Dietze
et al. 2014) who experienced verbal violence on their most
recent
occasion consuming more than 7 (women)/10 (men) standard drinks; and
characteristics of their nights.

Once parameters were determined (see Appendix A for further
details), the model was run 100 times to account for stochastic
variation and the output distribution properties (e.g. mean, median,
interquartile range) of the results were compared to available data.

Model
robustness

Many of the parameters in the model relate to the
likelihood of
individuals making particular decisions under specific circumstances;
for example p_PTrush_OU_plan_$ (Appendix A)—the probability that an
individual will choose to catch the last train home if they have less
than $50 left, had only planned to stay out for up to one hour longer
and live in an OU area. These types of features have been included
based on qualitative studies suggesting that they play a role in young
people's drinking events, with quantitative data either unavailable or
unfeasible to obtain for many of the related parameters. Nevertheless,
by including such features—even using authors' estimates for their
values—we believe the model has been improved, in particular as the
model outputs can now be probed for sensitivities when they vary.

Individual parameter variations

The differences in model outputs were measured when
parameters
were individually changed to test: a total of 50 friendship groups or
1000 friendship groups; a population of all women or all men; a
population of all 18–21 year olds or all 22–25 year olds; a population
of all IC residents or all OU residents; a total of 10 public venues or
500 public venues; all public venues niche or all public venues
commercial; individuals' planned length of nights distributed as
Poisson(6) or Poisson(10); individuals' drinking limits distributed as
Poisson(15/10) for men/women respectively or Poisson(25/20) for
men/women respectively; individuals never moving (unless the venue they
were in closed) or individuals moving each hour; no rush for the last
train or everyone rushing for the last train; no relative risk
differences for fights when drunk or in niche, private or commercial
venues or relative risks of 10 when drunk and 1:5:10 for
niche:private:commercial venues; no queues or queues of 0.75 hours and
0.33 hours for commercial and niche venues, respectively, all night; no
drink limits for public venues to eject patrons or public venues
ejecting patrons who had consumed greater than 15 (men)/10 (women)
standard drinks; no harms drink threshold or a threshold of 8 (men)/4
(women); no one going home after a verbal fight involving a member of
their friendship group or everyone going home; no one going home after
being in a venue that closed or everyone going home; and less expensive
taxis home ($10/$25 for IC/OU residents) and no required minimum money
to go to a public venue or more expensive taxis ($40/$80 for IC/OU
residents) and $50 minimum required to go to a public venue.

Uncertainty analysis using Latin Hypercube Sampling

In addition to understanding how individual parameter
changes
affect model outputs, Latin Hypercube Sampling (Helton
& Davis 2003; Iman
2008; Marino et al. 2008)
was used to test the
effects of jointly varying the above parameters. Continuous parameters
were considered to be uniformly distributed between their lower and
upper bounds, with 11 sample points (10 intervals) for each parameter.
To attempt to separate variation due to parameter changes from the
stochastic variation of the model, 10 simulations were run for each
hypercube parameter sample and the average outputs were used as
representatives of each point. The distribution of average outputs from
these 11^(number of parameters) hypercube sample points were compared
to the baseline point estimate distribution with stochastic variation.

The large number of parameters made it unfeasible to
perform this
experiment on all variables at once, and so parameters were tested in
five groups: 1) demographic parameters (gender, age, residence and
number of friendship groups); 2) harm-related parameters (drinking
limits, likelihood of going home after a fight, the harms drink
threshold and scaling factors for verbal fights in different venue
types and when drunk); 3) movement related parameters (planned length
of nights, likelihood of moving each hour, likelihood of going home
after a venue closes and likelihood of rushing for the last train); 4)
venue characteristics (number of venues, type of venues, queue times,
drink limits); and 5) costs (taxi price, money required to go out).

Results

Drinks consumed, amount spent and time spent drinking

Although the model produces realistic distributions for the
total
drinks consumed, amount spent and time individuals spent out on a big
night, there are several disparities between the model outputs and
reports from YAAS participants from the Rest of Melbourne (Figure 1). First, the distribution of drinks
consumed by
YAAS participants is truncated below 8, whereas the model is not. This
is due to selection bias in YAAS; participants were only recruited into
the study if they reported recently consuming more than 7 (women) or
more than 10 (men) drinks in a single session. Second, total amount
spent in the model was left shifted (less money spent) than the
data—again most likely owing to selection bias in YAAS—and the modelled
amounts spent were more evenly distributed than the YAAS. This may in
part be due to survey participants rounding their total spending or
starting their night out with more discrete amounts of money: when $50
bins were used to plot spending greater than $100 in the model, the fit
was slightly improved. Third, the very highly skewed length of the
drinking session from YAAS was not reproduced by the model, largely
owing to the less skewed Gamma and Poisson distributions used to set up
agents' planned lengths of night, drinking limits, drinking rates and
spending money. Nevertheless, the initial peak of around 6–7 hours
spent drinking was captured by the model.

Figure 1. SimDrink outputs
compared to the Young
Adults Alcohol Study (YAAS). Comparison of total drinks consumed (top
left), total amount spent (top right) and total time out (bottom)
between the model and the survey results for young people enrolled in
YAAS (excluding Hume and Yarra residents who were used to parametrise
the model) describing their most recent 'big night out'. Model results
include 95% confidence intervals from 100 simulations.

Harms experienced

The percentage of the modelled population who experienced
each
type of harm in the 100 simulations was measured. The median,
interquartile range (IQR) and extremes are shown in the boxplots of
Figure 2, compared to available
data. Over these
simulations, on a single night out a median of 6.33% (IQR 5.58–7.28%)
of people experienced verbal violence; 13.63% (IQR 12.88–14.20%) of
people drank more than their consumption limit; 25.16% (IQR
23.01–27.58%) witnessed verbal violence among their friendship group;
and 5.42% (IQR 4.73–6.59%) of people had difficulty getting home.

The only available data we found to compare this to (that
was not
used to determine model parameters) was the percentage of participants
in the YAAS follow-up wave who experienced verbal harms on their most
recent night out (6.18%), which was replicated well by the model.

Figure 2. Harms in SimDrink.
Median, interquartile
range and upper / lower bounds for the percentage of people in the
model who experienced verbal violence, drinking too much, a verbal
fight involving a member of their friendship group and difficulty
getting home. Results from 100 simulations.

Sensitivity of parameters

For each parameter variation, the prevalence of verbal
violence,
drinking too much and having trouble getting home on a given night out
are compared to best estimates in Figure 3.
Variations in outputs are logically valid and most are small, with the
greatest changes being in response to:

Population size: a larger number of friendship groups
resulted in a higher prevalence of verbal harms and an increase in the
variability of the percentage who experienced consumption-related harms
or difficulty getting home;

Gender: an all-male population resulted in more people
experiencing verbal harms (consistent with Table 1) and fewer people
experiencing consumption-related harms;

Planned length of night: an increase in the average
planned
length of night resulted in more people experiencing verbal harms
(consistent with Table 1),
consumption-related
harms and having difficulty getting home;

Drinking limits: higher drinking limits resulted in a
higher
prevalence of verbal harms and difficulty getting home and fewer people
experiencing consumption-related harms;

Frequency of moving between venues: a higher movement
frequency resulted in more people experiencing consumption-related
harms but fewer people experiencing verbal harms and difficulty getting
home (note that this does not directly compare to Table 1, since movement frequency
combines with planned
length of night to influence number of venues visited—see the Latin
Hypercube uncertainty analysis);

Last train: a certainty of rushing for the last train
resulted in a lower prevalence of verbal harms and fewer people
experiencing difficulty getting home (note that people from the IC can
still move from a private venue to a public venue after public
transport has finished, and so can still experience difficulty getting
home); and

Responsible service of alcohol (RSA): ejecting
intoxicated people from public venues sooner
resulted in a higher prevalence of verbal harms and fewer people
experiencing consumption-related harms or difficulty getting home.

Figure 3. Sensitivity of harms.
The effects on
verbal, consumption-related and transport-related harms of changes in:
population size; gender; age; residency; number of venues; venue types;
planned length of night and drinking limit distributions; the
likelihood of moving each hour; relative risks of fights when drunk or
in niche (nic), private (pri) and commercial (com) venues; queueing
times; responsible service of alcohol (RSA) enforcement; the harms
drink threshold; the likelihood of going home after a verbal fight; the
likelihood of going home after a venue closes; and travel limitations.
Box plots represent median, inter-quartile range and lower/upper bounds
from 100 simulation outputs.

Latin Hypercube uncertainty analysis

Relative to the stochastic variation of the model,
demographic,
harm-related and movement parameters played a significant role in the
prevalence of all three types of harm, while the venue and cost
parameters had little influence on model outcomes (Figure 4). In particular, there were some
samples of the
harm-related parameters that resulted in more than 30% of the
population experiencing verbal harms. This indicates that these
parameters are important to the model and assumptions about their
values should be detailed when using the model to make predictions.

Figure 4. Latin Hypercube
uncertainty analysis.
Blue boxplots: Variation in the average (after 10 simulations)
percentage of people experiencing harms when parameters are taken from
every point on the Latin Hypercube, for demographic parameters (gender,
age, residence and number of friendship groups), harm-related
parameters (drinking limits, likelihood of going home after a fight,
the harms drink threshold and scaling factors for verbal fights in
different venue types and when drunk), movement related parameters
(planned length of nights, likelihood of moving each hour, likelihood
of going home after a venue closes and likelihood of rushing for the
last train), venue characteristics (number of venues, type of venues,
queue times, drink limits), and costs (taxi price, money required to go
out). Black boxplot: stochastic variation from 100 simulations with
point estimate parameters.

Limitations

The model has several limitations owing to either its
complexity
or the lack of available data. First, limited studies are available
that could be used to estimate many of the parameters, and the current
calibration relies heavily on the YAAS data. In particular, using
participants from Yarra and Hume to calibrate IC and OU populations
respectively while keeping those living everywhere else for validation
may have introduced some bias, and parameters should be updated as
independent studies become available. Nevertheless, the model remains a
useful proof-of-concept tool, and we emphasize that it should be used
to compare multiple policy options rather than to directly estimate the
effects of individual policies. This is especially the case for
situations where particular sub-groups are of interest, since the model
is only calibrated to overall outcomes and the uncertainty analysis
suggests that differences in the simulated population may be important.
Second, even though the model allows large amounts of heterogeneity,
some properties are categorised, such as the age (categorised as 18–21
year olds and 22–25 year olds) and residence (categorised as OU or IC)
of individuals. It is unclear how, for example, the propensity of
individuals to rush to get the last train varies with distance from the
CBD, or how drinking limits and rates vary continuously as age
increases. However given the lack of data to investigate these
relationships, categorising such variables seems appropriate. Third,
agent characteristics have been drawn independently from probability
distributions while in practice these characteristics would exhibit
some degree of correlation among social groups. Should appropriate
individual-level data become available, adjustments are possible that
would enable the model to use joint probability distributions to
configure agent properties.

Further
work

Applications and model extensions

Further work with policy makers is required to identify
specific
harm reduction interventions that can be virtually implemented and
compared. For example, Melbourne City Council's Transport Strategy (City of Melbourne 2012) involves
improving the
late night accessibility of the CBD, one proposal being to extend
public transport operating hours. The effects of such a policy change
could be tested in this model and compared to alternate scenarios (for
example improvements to taxi availability). Other policies that aim
more explicitly to reduce alcohol related harms that could be tested
include: venue lockouts—where individuals are allowed to remain in
venues but no longer enter after a particular time (Department of Justice 2008;
Menéndez et al. 2015);
increasing the
taxation of alcohol (both on and off licence) (Skov
2009); changing venue operations by restricting opening hours
(Cobiac et al. 2009); and
training bar staff to
more strictly enforce responsible service of alcohol (Graham et al. 2004; Lang
et al. 1998). Each of these policies is likely to affect
different
groups in different ways (for example OU and IC residents, niche or
commercial venues), and changes to the prevalence of verbal,
consumption and transport-related harms—both direct and indirect—are
captured in the model. This will allow evidence based decisions to be
made on the most effective interventions, ensuring they have their
intended effects.

The high versatility of ABMs means that the model can
easily be
expanded as further data becomes available or in order to address
specific policy questions. For example, physical violence in the
night-time economy is a concern for police and policy makers, and if
data became available on the prevalence of experiencing physical
violence on a single night out, this feature could be included.
Methodological improvements could also be made. For example, by using
global positioning system co-ordinates for venue locations and
including more detailed neighbourhoods (with populations parametrised
by census data), the model could be used for a geo-simulation. In such
a scenario the accessibility of public transport could also be varied
across neighbourhoods. Finally, as more studies are undertaken to
understand the consumption event, different theoretical models could be
developed and tested regarding the distributions that have been assumed
for parameters such as the planned lengths of nights, drinking limits
and drinking rates, with outputs fit to observed data accordingly.

Areas identified for future alcohol studies

The construction of this model has identified many areas in
alcohol research that are lacking any empirical data. Most importantly
and perhaps surprisingly, no data could be identified on the prevalence
of consumption-related and transport-related harms on an individual
night out. Other parameters and distributions that were important for
this model, but could not be informed by sufficient data (limited or no
studies available), included individual drinking limits, planned
lengths of nights, the frequency of movements between venues and the
probability of individuals rushing to get the last train. Beyond their
significance for this model, these parameters would be extremely useful
for alcohol research more broadly, in particular in the context of
understanding the consumption event.

Conclusion

We have constructed a proof-of-concept ABM to simulate a
population of 18-25 year olds engaging in heavy sessional drinking on a
night out in Melbourne. The model includes demographic, setting and
situational-behavioural heterogeneity and produces realistic estimates
for the prevalence of various types of acute alcohol related harm. As
parameters vary across their domains changes in outputs are logically
valid and modest, indicating that the model is robust and internally
consistent. Further, the model is able to compare the indirect effects
of policy changes such as the displacement of individuals or venue
substitution, making it a particularly attractive for modelling policy
decisions and identifying the drivers behind overall statistics.

Acknowledgements

The research
reported here was funded by an Australian Research Council
Discovery Project (DP110101720). The authors gratefully acknowledge the
contribution to this work of the Victorian Operational Infrastructure
Support Program. The National Drug Research Institute is supported by
funding from the Australian Government under the Substance Misuse
Prevention and Service Improvement Grants Fund. NS is the recipient of
a Burnet Institute Jim and Margaret Beever Fellowship, PD is the
recipient of a National Health and Medical Research Council (NHMRC)
Senior Research Fellowship and ML is the recipient of an NHMRC Early
Career Fellowship.

For Hume and Yarra residents, at first venue
attended,
determine: mean drinking rate of (18-21 year old male) participants in
commercial venues / mean drinking rate of (18-21 year old male)
participants in private venues.
Average across age and sex categories.

Average for Hume and Yarra residents of: drinking
rate in
last venue of evening (for people ending in a private venue, having
attended two or more venues) / average drink rate in first venue (if it
was private).

Total amount spent by Hume and Yarra residents on
drinks
in commercial venues (including what others spent on them)/total drinks
they consumed there. Only includes venues where spending >0.
Similarly for niche and private venues.

$_nic

Drink price in niche venues.

$8.56

$_pri

Drink price in private venues.

$5.08

Movements

money2goout

Average spending money of friends required for
group to
go to public venue.

$30

Sensitivity analysis

Authors' estimate.

p_taxi

Probability of getting a taxi (per hour): number of
taxis
per 100 people in the model, assuming they are all available for one
trip per hour. I.e. pr(getting taxi each hour)=(#people/100) * p_taxi *
(1/taxiqueue).

1/100 people

Calibration

Parameter can be used to calibrate the percentage
of
people experiencing transport harms. Increases / decreases the number
of taxis in the model.

v_pt

Public transport travel speed.

25km/h

Sensitivity analysis

Used to define movement times in model.

v_nopt

Travel speed with no public transport.

10km/h

v_taxi

Taxi speed.

60 km/h

taxi$_OU

Cost of a taxi to Outer Urban private / home.

$50

taxi$_IC

Cost of a taxi to Inner City private / home.

$25

d_OUpri2OUpri

Agents travelling Outer Urban private-Outer Urban
private
will preference venues in this radius when public transport is
available.

15km

d_OUpri2OUpri_noPT

Agents travelling Outer Urban private-Outer Urban
private
will preference venues in this radius when public transport is not
available.

Dependent on scaling factors and
harms_drinkthreshold.
Let time_nic_m and time_nic_m_drunk be the total person hours in YAAS
spent by men in niche venues before and after harms_drinkthreshold
drinks were consumed respectively. For venues where the drink threshold
is crossed, all time is counted towards time_nic_m_drunk.

MACLEAN,
S., & Callinan, S.
(2013). "Fourteen Dollars for One Beer!" Pre-drinking is associated
with high-risk drinking among Victorian young adults. Australian
and New Zealand journal of public health, 37(6), 579–585. [doi:10.1111/1753-6405.12138]